AI Inference Engineering
How trained models actually get served in production — quantization, batching, KV caching, speculative decoding, GPU internals, benchmarking and the hardware underneath.
Written byRohan Sharma
Fundamentals
3 lessons- 9 min read
What Is Inference Engineering
Why serving a trained model is a completely different engineering problem from training one, and what "inference engineering" actually covers.
- 10 min read
Autoregressive Decoding, Step by Step
How a transformer actually produces one token at a time — the prefill pass, the decode loop, and why the two phases have opposite performance characteristics.
- 11 min read
The KV Cache
The single most important data structure in LLM serving — what it stores, why it's so large, and why managing it well or badly determines your entire system's throughput.
Fundamentals Quiz
6 questions
Model Optimization
3 lessons- 11 min read
Quantization
Trading numerical precision for memory and bandwidth — the highest-leverage single technique in inference engineering.
- 9 min read
Pruning and Distillation
Making a model smaller by removing what it doesn't need, or by teaching a smaller model to imitate a larger one.
- 10 min read
Speculative Decoding
Using a small, fast model to guess ahead and a large model to verify in parallel — getting large-model quality at closer to small-model speed.
Model Optimization Quiz
8 questions
Serving Systems
4 lessons- 10 min read
Continuous Batching
Why batching requests together is essential for throughput, why naive batching wastes it anyway, and how continuous batching fixes that.
- 10 min read
PagedAttention and KV Cache Memory Management
Borrowing virtual memory paging from operating systems to stop the KV cache from wasting most of a GPU's memory.
- 9 min read
Disaggregated Prefill/Decode Serving
Why the two halves of serving a request fight over the same GPU, and what happens when you give them separate hardware entirely.
- 10 min read
Serving Frameworks and Observability
What an inference server actually does end to end, the frameworks that implement it, and the metrics that tell you whether it's healthy.
Serving Systems Quiz
6 questions
Hardware & Compilation
3 lessons- 10 min read
GPU Architecture for Inference
What is actually inside the chip your model runs on, explained simply — and why its memory, not its math speed, usually decides how fast you get an answer.
- 9 min read
Kernel Fusion and Compilation
Why running a model "as written" leaves speed on the table, and how compilers rewrite it into far fewer, larger steps without changing the answer.
- 10 min read
FlashAttention and IO-Aware Kernels
How rewriting attention to avoid ever writing its biggest intermediate result to slow memory made long-context models practical.
Hardware & Compilation Quiz
6 questions
Advanced Serving Techniques
4 lessons- 9 min read
Serving Many Fine-Tunes at Once with Multi-LoRA
A company might have 500 customers, each with their own fine-tuned model. Do you really need 500 copies of a 140 GB model? No — here's why.
- 9 min read
Structured Output and Guided Decoding
How to make a model that predicts free-form text guarantee its output is always valid JSON, or always matches a fixed grammar — without retraining it.
- 9 min read
Model Routing and Semantic Caching
You don't need your biggest, most expensive model to answer "what's 2+2". Sending every request to the same model is leaving money on the table.
- 10 min read
Test-Time Compute and Reasoning Models
Some models get smarter by thinking longer before answering. That changes the entire cost equation this course has built up — here's how.
Advanced Serving Techniques Quiz
6 questions
Benchmarking & Evaluation
5 lessons- 8 min read
Why Benchmark an Inference System
You cannot make a system faster or cheaper by guessing. Why benchmarking is the thing that turns every other lesson in this course from theory into a real decision.
- 11 min read
What to Measure — Metrics Defined
The exact numbers a serving benchmark reports, what each one means in plain terms, and the formula behind every one of them.
- 11 min read
How to Benchmark — Methodology That Doesn't Lie to You
Averages hide the truth, one lucky run proves nothing, and testing on easy traffic tells you nothing about the traffic that will actually hurt you. How to run a benchmark you can trust.
- 10 min read
Correctness and Quality Benchmarks
Speed means nothing if the answer is wrong. How to check correctness for tasks with one right answer, and how to evaluate quality for tasks that don't.
- 9 min read
Building Your Own Benchmark
A hands-on walkthrough of writing a real benchmarking script — what to measure, which tools to reach for, and what the output should actually look like.
Benchmarking & Evaluation Quiz
10 questions
Scaling & Deployment
3 lessons- 10 min read
Parallelism Strategies for Serving
What to do when a model doesn't fit on one GPU — splitting the model itself, not just the batch, across multiple accelerators.
- 9 min read
Hardware and Cost Modeling
What actually differs between accelerators, and how to turn latency and throughput numbers into a defensible cost-per-token figure.
- 11 min read
Designing a Production Inference System
Pulling every technique in this course into a single worked decision process for a real deployment.
Scaling & Deployment Quiz
6 questions
Assessments
8 quizzesFundamentals Quiz
Fundamentals · 6 questions
Model Optimization Quiz
Model Optimization · 8 questions
Serving Systems Quiz
Serving Systems · 6 questions
Hardware & Compilation Quiz
Hardware & Compilation · 6 questions
Advanced Serving Techniques Quiz
Advanced Serving Techniques · 6 questions
Benchmarking & Evaluation Quiz
Benchmarking & Evaluation · 10 questions
Scaling & Deployment Quiz
Scaling & Deployment · 6 questions
AI Inference Engineering Final Assessment
Final assessment · 15 questions